Unsupervised Learning for Brain-Computer Interfaces Based on Event-Related Potentials: Review and Online Comparison [Research Frontier]

David Hübner, Thibault Verhoeven, Klaus Robert Müller, Pieter Jan Kindermans, Michael Tangermann

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)

Abstract

One of the fundamental challenges in brain-computer interfaces (BCIs) is to tune a brain signal decoder to reliably detect a user's intention. While information about the decoder can partially be transferred between subjects or sessions, optimal decoding performance can only be reached with novel data from the current session. Thus, it is preferable to learn from unlabeled data gained from the actual usage of the BCI application instead of conducting a calibration recording prior to BCI usage. We review such unsupervised machine learning methods for BCIs based on event-related potentials of the electroencephalogram. We present results of an online study with twelve healthy participants controlling a visual speller. Online performance is reported for three completely unsupervised learning methods: (1) learning from label proportions, (2) an expectation-maximization approach and (3) MIX, which combines the strengths of the two other methods. After a short ramp-up, we observed that the MIX method not only defeats its two unsupervised competitors but even performs on par with a state-of-the-art regularized linear discriminant analysis trained on the same number of data points and with full label access. With this online study, we deliver the best possible proof in BCI that an unsupervised decoding method can in practice render a supervised method unnecessary. This is possible despite skipping the calibration, without losing much performance and with the prospect of continuous improvement over a session. Thus, our findings pave the way for a transition from supervised to unsupervised learning methods in BCIs based on eventrelated potentials.

Original languageEnglish
Pages (from-to)66-77
Number of pages12
JournalIEEE Computational Intelligence Magazine
Volume13
Issue number2
DOIs
Publication statusPublished - 2018 May

Bibliographical note

Funding Information:
DH and MT thankfully acknowledge the support by BrainLinks-BrainTools Cluster of Excellence funded by the German Research Foundation (DFG), grant number EXC 1086. DH and MT further acknowledge the bwHPC initiative, grant INST 39/963-1 FUGG. PJK gratefully acknowledges funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement NO 657679. TV thankfully acknowledges financial support from the Special Research Fund of Ghent University. KRM thanks DFG (DFG SPP 1527, MU 987/14-1) and the Federal Ministry for Education and Research (BMBF No. 2017-0-00451) as well as support by the Brain Korea 21 Plus Program by the Institute for Information & Communications Technology Promotion (IITP) grant (1IS14013A) funded by the Korean government.

Funding Information:
DH and MT thankfully acknowledge the support by BrainLinks-BrainTools Cluster of Excellence funded by the German Research Foundation (DFG), grant number EXC 1086. DH and MT further acknowledge the bwHPC initiative, grant INST 39/963-1 FUGG. PJK gratefully acknowledges funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement NO 657679. TV thankfully acknowledges financial support from the Special Research Fund of Ghent University. KRM thanks DFG (DFG SPP 1527, MU 987/14-1) and the Federal Ministry for Education and Research (BMBF No. 2017-0-00451) as well as support by the Brain Korea 21 Plus Program by the Institute for Information & Communications Technology Promotion (IITP) grant (1IS14013A) funded by the Korean government.

Publisher Copyright:
© 2018 IEEE.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'Unsupervised Learning for Brain-Computer Interfaces Based on Event-Related Potentials: Review and Online Comparison [Research Frontier]'. Together they form a unique fingerprint.

Cite this